Heart is essential organ of human body. For patients having cardiovascular diseases(CVD), it is important to monitor physiology of heart effectively. In study, Magnetic Resonance Images(MRI) are analyzed using convolution neural network for heart segmentation. The dataset is downloaded from medical segmentation decathlon (MSD) challenge dataset. 20 heart MRI scans are used for study. The images obtained from 3D scans are used as training, validation and test dataset. Five different convolution neural network models are built and their performance is evaluated using different performance metrics like TPR, FPR, Jaccard index, Youden index etc. It is concluded that deep learning can effectively predict the heart segmentation from MRI images.
Input variables : Magnetic Resonance Image
Output Variables : Heart Segmentation Image
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | November, 2019 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Health Risk Prediction |